Overview

Dataset statistics

Number of variables19
Number of observations576
Missing cells1999
Missing cells (%)18.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory85.6 KiB
Average record size in memory152.2 B

Variable types

Numeric14
Categorical5

Alerts

brand_name has a high cardinality: 105 distinct valuesHigh cardinality
generic_name has a high cardinality: 102 distinct valuesHigh cardinality
beneficiary_count_lis has a high cardinality: 275 distinct valuesHigh cardinality
beneficiary_count_no_lis has a high cardinality: 274 distinct valuesHigh cardinality
total_spending is highly overall correlated with beneficiary_cost_share and 6 other fieldsHigh correlation
beneficiary_count is highly overall correlated with unit_count and 4 other fieldsHigh correlation
unit_count is highly overall correlated with beneficiary_count and 3 other fieldsHigh correlation
beneficiary_cost_share is highly overall correlated with total_spending and 4 other fieldsHigh correlation
total_annual_spending_per_user is highly overall correlated with total_spending and 4 other fieldsHigh correlation
average_cost_per_unit is highly overall correlated with total_annual_spending_per_user and 2 other fieldsHigh correlation
average_annual_beneficiary_cost_share is highly overall correlated with total_spending and 4 other fieldsHigh correlation
claim_count is highly overall correlated with beneficiary_count and 3 other fieldsHigh correlation
beneficiary_cost_share_lis is highly overall correlated with total_spending and 6 other fieldsHigh correlation
beneficiary_cost_share_no_lis is highly overall correlated with total_spending and 7 other fieldsHigh correlation
average_beneficiary_cost_share_lis is highly overall correlated with total_spending and 6 other fieldsHigh correlation
average_beneficiary_cost_share_no_lis is highly overall correlated with total_spending and 4 other fieldsHigh correlation
coverage_type is highly overall correlated with average_annual_beneficiary_cost_share and 4 other fieldsHigh correlation
average_annual_beneficiary_cost_share has 307 (53.3%) missing valuesMissing
beneficiary_count_lis has 282 (49.0%) missing valuesMissing
beneficiary_count_no_lis has 282 (49.0%) missing valuesMissing
beneficiary_cost_share_lis has 282 (49.0%) missing valuesMissing
beneficiary_cost_share_no_lis has 282 (49.0%) missing valuesMissing
average_beneficiary_cost_share_lis has 282 (49.0%) missing valuesMissing
average_beneficiary_cost_share_no_lis has 282 (49.0%) missing valuesMissing
brand_name is uniformly distributedUniform
generic_name is uniformly distributedUniform
serialid is uniformly distributedUniform
serialid has unique valuesUnique
total_spending has 37 (6.4%) zerosZeros
beneficiary_count has 37 (6.4%) zerosZeros
unit_count has 37 (6.4%) zerosZeros
beneficiary_cost_share has 42 (7.3%) zerosZeros
total_annual_spending_per_user has 37 (6.4%) zerosZeros
average_cost_per_unit has 37 (6.4%) zerosZeros
average_annual_beneficiary_cost_share has 10 (1.7%) zerosZeros
claim_count has 37 (6.4%) zerosZeros
beneficiary_cost_share_lis has 19 (3.3%) zerosZeros
beneficiary_cost_share_no_lis has 19 (3.3%) zerosZeros
average_beneficiary_cost_share_lis has 19 (3.3%) zerosZeros
average_beneficiary_cost_share_no_lis has 19 (3.3%) zerosZeros

Reproduction

Analysis started2023-09-18 19:22:00.148500
Analysis finished2023-09-18 19:22:36.702231
Duration36.55 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

year
Real number (ℝ)

Distinct6
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.5
Minimum2010
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:36.752525image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12011
median2012.5
Q32014
95-th percentile2015
Maximum2015
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6258777
Coefficient of variation (CV)0.00080788954
Kurtosis-1.1758501
Mean2012.5
Median Absolute Deviation (MAD)1.5
Skewness0
Sum1159200
Variance2.6434783
MonotonicityNot monotonic
2023-09-18T19:22:36.936105image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2011 104
18.1%
2012 104
18.1%
2013 104
18.1%
2014 104
18.1%
2010 80
13.9%
2015 80
13.9%
ValueCountFrequency (%)
2010 80
13.9%
2011 104
18.1%
2012 104
18.1%
2013 104
18.1%
2014 104
18.1%
2015 80
13.9%
ValueCountFrequency (%)
2015 80
13.9%
2014 104
18.1%
2013 104
18.1%
2012 104
18.1%
2011 104
18.1%
2010 80
13.9%

brand_name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct105
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
Depo-Medrol; Methylprednisolone Acetate
 
10
Abilify
 
6
Lucentis
 
6
Prezista
 
6
Perforomist
 
6
Other values (100)
542 

Length

Max length39
Median length32
Mean length11.454861
Min length5

Characters and Unicode

Total characters6598
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.3%

Sample

1st rowAbilify
2nd rowAbilify
3rd rowAbilify
4th rowAbilify
5th rowAbilify

Common Values

ValueCountFrequency (%)
Depo-Medrol; Methylprednisolone Acetate 10
 
1.7%
Abilify 6
 
1.0%
Lucentis 6
 
1.0%
Prezista 6
 
1.0%
Perforomist 6
 
1.0%
Orencia 6
 
1.0%
Nexium 6
 
1.0%
Neulasta 6
 
1.0%
Lyrica 6
 
1.0%
Letairis 6
 
1.0%
Other values (95) 512
88.9%

Length

2023-09-18T19:22:37.152636image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hcl 25
 
3.0%
acetate 15
 
1.8%
lar 15
 
1.8%
sandostatin 15
 
1.8%
lantus 12
 
1.5%
depo-medrol 10
 
1.2%
activase 10
 
1.2%
sulfate 10
 
1.2%
methylprednisolone 10
 
1.2%
humira 7
 
0.9%
Other values (126) 692
84.3%

Most occurring characters

ValueCountFrequency (%)
a 642
 
9.7%
e 594
 
9.0%
i 514
 
7.8%
o 415
 
6.3%
n 400
 
6.1%
r 396
 
6.0%
t 344
 
5.2%
l 335
 
5.1%
245
 
3.7%
s 206
 
3.1%
Other values (46) 2507
38.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5344
81.0%
Uppercase Letter 892
 
13.5%
Space Separator 245
 
3.7%
Other Punctuation 91
 
1.4%
Dash Punctuation 16
 
0.2%
Decimal Number 10
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 642
12.0%
e 594
11.1%
i 514
 
9.6%
o 415
 
7.8%
n 400
 
7.5%
r 396
 
7.4%
t 344
 
6.4%
l 335
 
6.3%
s 206
 
3.9%
c 189
 
3.5%
Other values (15) 1309
24.5%
Uppercase Letter
ValueCountFrequency (%)
A 107
12.0%
C 95
 
10.7%
P 67
 
7.5%
H 60
 
6.7%
S 59
 
6.6%
G 57
 
6.4%
L 56
 
6.3%
T 51
 
5.7%
R 49
 
5.5%
D 46
 
5.2%
Other values (15) 245
27.5%
Other Punctuation
ValueCountFrequency (%)
; 81
89.0%
. 10
 
11.0%
Decimal Number
ValueCountFrequency (%)
1 5
50.0%
3 5
50.0%
Space Separator
ValueCountFrequency (%)
245
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6236
94.5%
Common 362
 
5.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 642
 
10.3%
e 594
 
9.5%
i 514
 
8.2%
o 415
 
6.7%
n 400
 
6.4%
r 396
 
6.4%
t 344
 
5.5%
l 335
 
5.4%
s 206
 
3.3%
c 189
 
3.0%
Other values (40) 2201
35.3%
Common
ValueCountFrequency (%)
245
67.7%
; 81
 
22.4%
- 16
 
4.4%
. 10
 
2.8%
1 5
 
1.4%
3 5
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 642
 
9.7%
e 594
 
9.0%
i 514
 
7.8%
o 415
 
6.3%
n 400
 
6.1%
r 396
 
6.0%
t 344
 
5.2%
l 335
 
5.1%
245
 
3.7%
s 206
 
3.1%
Other values (46) 2507
38.0%

generic_name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct102
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
Immun Glob G(IGG)/Pro/Iga 0-50
 
12
Immun Glob G (IGG)/Gly/Iga 50+
 
12
Insulin Glargine,Hum.Rec.Anlog
 
11
Aripiprazole
 
6
Ambrisentan
 
6
Other values (97)
529 

Length

Max length74
Median length26
Mean length17.824653
Min length7

Characters and Unicode

Total characters10267
Distinct characters58
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowAripiprazole
2nd rowAripiprazole
3rd rowAripiprazole
4th rowAripiprazole
5th rowAripiprazole

Common Values

ValueCountFrequency (%)
Immun Glob G(IGG)/Pro/Iga 0-50 12
 
2.1%
Immun Glob G (IGG)/Gly/Iga 50+ 12
 
2.1%
Insulin Glargine,Hum.Rec.Anlog 11
 
1.9%
Aripiprazole 6
 
1.0%
Ambrisentan 6
 
1.0%
Formoterol Fumarate 6
 
1.0%
Abatacept; Abatacept/Maltose 6
 
1.0%
Esomeprazole Magnesium 6
 
1.0%
Pegfilgrastim 6
 
1.0%
Pregabalin 6
 
1.0%
Other values (92) 499
86.6%

Length

2023-09-18T19:22:37.384866image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hcl 57
 
5.5%
acetate 32
 
3.1%
glob 29
 
2.8%
immun 29
 
2.8%
sodium 21
 
2.0%
50 17
 
1.6%
alfa 16
 
1.6%
g 12
 
1.2%
igg)/gly/iga 12
 
1.2%
treprostinil 12
 
1.2%
Other values (133) 794
77.0%

Most occurring characters

ValueCountFrequency (%)
e 856
 
8.3%
i 820
 
8.0%
a 810
 
7.9%
o 677
 
6.6%
t 628
 
6.1%
l 590
 
5.7%
r 566
 
5.5%
n 531
 
5.2%
455
 
4.4%
m 453
 
4.4%
Other values (48) 3881
37.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8080
78.7%
Uppercase Letter 1288
 
12.5%
Space Separator 455
 
4.4%
Other Punctuation 185
 
1.8%
Decimal Number 112
 
1.1%
Open Punctuation 45
 
0.4%
Close Punctuation 45
 
0.4%
Dash Punctuation 40
 
0.4%
Math Symbol 17
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 856
10.6%
i 820
10.1%
a 810
10.0%
o 677
 
8.4%
t 628
 
7.8%
l 590
 
7.3%
r 566
 
7.0%
n 531
 
6.6%
m 453
 
5.6%
u 324
 
4.0%
Other values (13) 1825
22.6%
Uppercase Letter
ValueCountFrequency (%)
G 156
12.1%
A 145
11.3%
I 127
9.9%
C 126
9.8%
P 98
 
7.6%
S 76
 
5.9%
H 73
 
5.7%
E 73
 
5.7%
T 70
 
5.4%
M 63
 
4.9%
Other values (9) 281
21.8%
Decimal Number
ValueCountFrequency (%)
0 51
45.5%
5 29
25.9%
1 11
 
9.8%
2 6
 
5.4%
8 5
 
4.5%
4 5
 
4.5%
3 5
 
4.5%
Other Punctuation
ValueCountFrequency (%)
/ 123
66.5%
. 22
 
11.9%
, 22
 
11.9%
; 18
 
9.7%
Space Separator
ValueCountFrequency (%)
455
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%
Math Symbol
ValueCountFrequency (%)
+ 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9368
91.2%
Common 899
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 856
 
9.1%
i 820
 
8.8%
a 810
 
8.6%
o 677
 
7.2%
t 628
 
6.7%
l 590
 
6.3%
r 566
 
6.0%
n 531
 
5.7%
m 453
 
4.8%
u 324
 
3.5%
Other values (32) 3113
33.2%
Common
ValueCountFrequency (%)
455
50.6%
/ 123
 
13.7%
0 51
 
5.7%
( 45
 
5.0%
) 45
 
5.0%
- 40
 
4.4%
5 29
 
3.2%
. 22
 
2.4%
, 22
 
2.4%
; 18
 
2.0%
Other values (6) 49
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10267
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 856
 
8.3%
i 820
 
8.0%
a 810
 
7.9%
o 677
 
6.6%
t 628
 
6.1%
l 590
 
5.7%
r 566
 
5.5%
n 531
 
5.2%
455
 
4.4%
m 453
 
4.4%
Other values (48) 3881
37.8%

coverage_type
Categorical

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size4.6 KiB
Part D
296 
Part B
280 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters3456
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPart D
2nd rowPart D
3rd rowPart D
4th rowPart D
5th rowPart D

Common Values

ValueCountFrequency (%)
Part D 296
51.4%
Part B 280
48.6%

Length

2023-09-18T19:22:37.598287image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-18T19:22:37.785475image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
ValueCountFrequency (%)
part 576
50.0%
d 296
25.7%
b 280
24.3%

Most occurring characters

ValueCountFrequency (%)
P 576
16.7%
a 576
16.7%
r 576
16.7%
t 576
16.7%
576
16.7%
D 296
8.6%
B 280
8.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1728
50.0%
Uppercase Letter 1152
33.3%
Space Separator 576
 
16.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 576
50.0%
D 296
25.7%
B 280
24.3%
Lowercase Letter
ValueCountFrequency (%)
a 576
33.3%
r 576
33.3%
t 576
33.3%
Space Separator
ValueCountFrequency (%)
576
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2880
83.3%
Common 576
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 576
20.0%
a 576
20.0%
r 576
20.0%
t 576
20.0%
D 296
10.3%
B 280
9.7%
Common
ValueCountFrequency (%)
576
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3456
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 576
16.7%
a 576
16.7%
r 576
16.7%
t 576
16.7%
576
16.7%
D 296
8.6%
B 280
8.1%

total_spending
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct537
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4227844 × 108
Minimum0
Maximum7.0306335 × 109
Zeros37
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:37.928604image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q134557234
median1.9942845 × 108
Q35.3967443 × 108
95-th percentile1.7337103 × 109
Maximum7.0306335 × 109
Range7.0306335 × 109
Interquartile range (IQR)5.0511719 × 108

Descriptive statistics

Standard deviation6.5057385 × 108
Coefficient of variation (CV)1.4709599
Kurtosis21.306611
Mean4.4227844 × 108
Median Absolute Deviation (MAD)1.8736281 × 108
Skewness3.3784316
Sum2.5475238 × 1011
Variance4.2324633 × 1017
MonotonicityNot monotonic
2023-09-18T19:22:38.174494image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
6.4%
303563688.7 2
 
0.3%
275984353.3 2
 
0.3%
234803146 2
 
0.3%
1225884701 1
 
0.2%
62533673.24 1
 
0.2%
34647375.24 1
 
0.2%
40143407.22 1
 
0.2%
47622607.16 1
 
0.2%
54257433.85 1
 
0.2%
Other values (527) 527
91.5%
ValueCountFrequency (%)
0 37
6.4%
104626.93 1
 
0.2%
630784.32 1
 
0.2%
1039890.46 1
 
0.2%
1084689.44 1
 
0.2%
1293081.32 1
 
0.2%
1352432.82 1
 
0.2%
1563550.5 1
 
0.2%
1571232.83 1
 
0.2%
3042836.26 1
 
0.2%
ValueCountFrequency (%)
7030633486 1
0.2%
4359504167 1
0.2%
3106960981 1
0.2%
2883122484 1
0.2%
2660421777 1
0.2%
2543786426 1
0.2%
2527319032 1
0.2%
2526306069 1
0.2%
2276374749 1
0.2%
2270015726 1
0.2%

beneficiary_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct536
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean218741.39
Minimum0
Maximum5698568
Zeros37
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:38.575037image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18639.75
median26758
Q3171911.75
95-th percentile1133103.2
Maximum5698568
Range5698568
Interquartile range (IQR)163272

Descriptive statistics

Standard deviation441777
Coefficient of variation (CV)2.0196315
Kurtosis42.128881
Mean218741.39
Median Absolute Deviation (MAD)24801.5
Skewness4.6244726
Sum1.2599504 × 108
Variance1.9516692 × 1011
MonotonicityNot monotonic
2023-09-18T19:22:38.808367image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
6.4%
9336 3
 
0.5%
10144 2
 
0.3%
9790 2
 
0.3%
31687 1
 
0.2%
13678 1
 
0.2%
12804 1
 
0.2%
28423 1
 
0.2%
31558 1
 
0.2%
28204 1
 
0.2%
Other values (526) 526
91.3%
ValueCountFrequency (%)
0 37
6.4%
65 1
 
0.2%
330 1
 
0.2%
475 1
 
0.2%
482 1
 
0.2%
533 1
 
0.2%
569 1
 
0.2%
643 1
 
0.2%
743 1
 
0.2%
829 1
 
0.2%
ValueCountFrequency (%)
5698568 1
0.2%
1752704 1
0.2%
1733071 1
0.2%
1732787 1
0.2%
1673911 1
0.2%
1598181 1
0.2%
1556034 1
0.2%
1527217 1
0.2%
1516292 1
0.2%
1496093 1
0.2%

unit_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct537
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48747854
Minimum0
Maximum4.9296252 × 108
Zeros37
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:39.043761image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11496759.8
median5723160.5
Q329186222
95-th percentile3.1085136 × 108
Maximum4.9296252 × 108
Range4.9296252 × 108
Interquartile range (IQR)27689462

Descriptive statistics

Standard deviation97253161
Coefficient of variation (CV)1.9950245
Kurtosis5.8611396
Mean48747854
Median Absolute Deviation (MAD)5627013
Skewness2.5273143
Sum2.8078764 × 1010
Variance9.4581773 × 1015
MonotonicityNot monotonic
2023-09-18T19:22:39.302611image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
6.4%
2316230 2
 
0.3%
2240840 2
 
0.3%
2017851 2
 
0.3%
71848190 1
 
0.2%
10760804 1
 
0.2%
3051652 1
 
0.2%
8649840 1
 
0.2%
10072377 1
 
0.2%
10758166 1
 
0.2%
Other values (527) 527
91.5%
ValueCountFrequency (%)
0 37
6.4%
1988 1
 
0.2%
2737 1
 
0.2%
4853 1
 
0.2%
5125 1
 
0.2%
5536 1
 
0.2%
6333 1
 
0.2%
6666 1
 
0.2%
7829 1
 
0.2%
9325 1
 
0.2%
ValueCountFrequency (%)
492962520 1
0.2%
460372139 1
0.2%
456213793 1
0.2%
452555226 1
0.2%
448605068 1
0.2%
436743044 1
0.2%
430415878 1
0.2%
429548239 1
0.2%
423148377 1
0.2%
419224451 1
0.2%

beneficiary_cost_share
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct532
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50261013
Minimum0
Maximum3.9106178 × 108
Zeros42
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:39.538022image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13365941.9
median22056618
Q354989956
95-th percentile2.165789 × 108
Maximum3.9106178 × 108
Range3.9106178 × 108
Interquartile range (IQR)51624014

Descriptive statistics

Standard deviation72825364
Coefficient of variation (CV)1.4489434
Kurtosis4.3647582
Mean50261013
Median Absolute Deviation (MAD)20201756
Skewness2.1354517
Sum2.8950343 × 1010
Variance5.3035336 × 1015
MonotonicityNot monotonic
2023-09-18T19:22:39.780510image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
 
7.3%
46824924.09 2
 
0.3%
60866329.06 2
 
0.3%
54778931.51 2
 
0.3%
10097210.56 1
 
0.2%
992662.12 1
 
0.2%
712351.44 1
 
0.2%
680763.1 1
 
0.2%
8466333.24 1
 
0.2%
38186677.97 1
 
0.2%
Other values (522) 522
90.6%
ValueCountFrequency (%)
0 42
7.3%
30434.69 1
 
0.2%
33864.07 1
 
0.2%
233476.65 1
 
0.2%
267447.16 1
 
0.2%
274701.47 1
 
0.2%
275398.22 1
 
0.2%
375288.2 1
 
0.2%
385473.14 1
 
0.2%
410124.45 1
 
0.2%
ValueCountFrequency (%)
391061781.2 1
0.2%
388902182.1 1
0.2%
370196109.4 1
0.2%
369502754.2 1
0.2%
347673799.6 1
0.2%
299402108.4 1
0.2%
292242339.6 1
0.2%
290093486.2 1
0.2%
286693327 1
0.2%
286537261.5 1
0.2%

total_annual_spending_per_user
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct536
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16635.697
Minimum0
Maximum162370.93
Zeros37
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:40.026333image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1442.22
median5640.76
Q321529.178
95-th percentile71946.902
Maximum162370.93
Range162370.93
Interquartile range (IQR)21086.958

Descriptive statistics

Standard deviation25960.796
Coefficient of variation (CV)1.5605476
Kurtosis8.1891778
Mean16635.697
Median Absolute Deviation (MAD)5617.08
Skewness2.6507178
Sum9582161.3
Variance6.7396294 × 108
MonotonicityNot monotonic
2023-09-18T19:22:40.264673image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
6.4%
58.25 2
 
0.3%
29925.44 2
 
0.3%
28190.43 2
 
0.3%
25150.29 2
 
0.3%
2062.8 1
 
0.2%
2705.98 1
 
0.2%
1412.36 1
 
0.2%
1509.05 1
 
0.2%
1712.29 1
 
0.2%
Other values (526) 526
91.3%
ValueCountFrequency (%)
0 37
6.4%
1.58 1
 
0.2%
1.69 1
 
0.2%
2.35 1
 
0.2%
4.77 1
 
0.2%
6.03 1
 
0.2%
6.23 1
 
0.2%
6.56 1
 
0.2%
7.61 1
 
0.2%
7.91 1
 
0.2%
ValueCountFrequency (%)
162370.93 1
0.2%
145481.62 1
0.2%
144069.54 1
0.2%
141673.75 1
0.2%
141147.79 1
0.2%
140734.52 1
0.2%
133844.66 1
0.2%
133420.75 1
0.2%
108013.82 1
0.2%
107489.13 1
0.2%

average_cost_per_unit
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct498
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean513.87167
Minimum0
Maximum35205.98
Zeros37
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:40.508460image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.635
median20.475
Q393.2725
95-th percentile1131.0175
Maximum35205.98
Range35205.98
Interquartile range (IQR)89.6375

Descriptive statistics

Standard deviation3170.4312
Coefficient of variation (CV)6.1696945
Kurtosis99.444899
Mean513.87167
Median Absolute Deviation (MAD)20.205
Skewness9.8306915
Sum295990.08
Variance10051634
MonotonicityNot monotonic
2023-09-18T19:22:40.749843image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
6.4%
0.64 3
 
0.5%
0.33 3
 
0.5%
0.12 3
 
0.5%
0.06 3
 
0.5%
0.2 3
 
0.5%
0.16 3
 
0.5%
0.43 3
 
0.5%
0.52 2
 
0.3%
8.02 2
 
0.3%
Other values (488) 514
89.2%
ValueCountFrequency (%)
0 37
6.4%
0.05 2
 
0.3%
0.06 3
 
0.5%
0.08 1
 
0.2%
0.09 2
 
0.3%
0.11 2
 
0.3%
0.12 3
 
0.5%
0.13 1
 
0.2%
0.16 3
 
0.5%
0.17 2
 
0.3%
ValueCountFrequency (%)
35205.98 1
0.2%
33865.51 1
0.2%
32857.47 1
0.2%
32775.67 1
0.2%
32301.81 1
0.2%
6827.41 1
0.2%
6497.74 1
0.2%
6158.54 1
0.2%
5977.25 1
0.2%
5303.69 1
0.2%

average_annual_beneficiary_cost_share
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct257
Distinct (%)95.5%
Missing307
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean3676.3968
Minimum0
Maximum29287.8
Zeros10
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:40.995301image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.258
Q1403.31
median2420.28
Q34493.4
95-th percentile13575.646
Maximum29287.8
Range29287.8
Interquartile range (IQR)4090.09

Descriptive statistics

Standard deviation5202.5846
Coefficient of variation (CV)1.4151314
Kurtosis10.787407
Mean3676.3968
Median Absolute Deviation (MAD)2056.38
Skewness3.0378041
Sum988950.74
Variance27066886
MonotonicityNot monotonic
2023-09-18T19:22:41.219603image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10
 
1.7%
6000.23 2
 
0.3%
5595.4 2
 
0.3%
5015.52 2
 
0.3%
2998.88 1
 
0.2%
2664.84 1
 
0.2%
2847.92 1
 
0.2%
3470.05 1
 
0.2%
4368.86 1
 
0.2%
297.87 1
 
0.2%
Other values (247) 247
42.9%
(Missing) 307
53.3%
ValueCountFrequency (%)
0 10
1.7%
0.41 1
 
0.2%
0.43 1
 
0.2%
0.64 1
 
0.2%
1.13 1
 
0.2%
1.45 1
 
0.2%
1.48 1
 
0.2%
1.54 1
 
0.2%
1.74 1
 
0.2%
1.86 1
 
0.2%
ValueCountFrequency (%)
29287.8 1
0.2%
29107.73 1
0.2%
28659.52 1
0.2%
28236.3 1
0.2%
28151.88 1
0.2%
27192.12 1
0.2%
21863.51 1
0.2%
20240.43 1
0.2%
19295.76 1
0.2%
17553.24 1
0.2%

claim_count
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct537
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean967023.18
Minimum0
Maximum9073952
Zeros37
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:41.462378image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q154981.75
median162238
Q3727304.75
95-th percentile5320156.2
Maximum9073952
Range9073952
Interquartile range (IQR)672323

Descriptive statistics

Standard deviation1811337.1
Coefficient of variation (CV)1.8731061
Kurtosis5.7301852
Mean967023.18
Median Absolute Deviation (MAD)151537.5
Skewness2.4753407
Sum5.5700535 × 108
Variance3.280942 × 1012
MonotonicityNot monotonic
2023-09-18T19:22:41.844561image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
6.4%
79626 2
 
0.3%
77085 2
 
0.3%
70070 2
 
0.3%
2296081 1
 
0.2%
180067 1
 
0.2%
23506 1
 
0.2%
145479 1
 
0.2%
169474 1
 
0.2%
180799 1
 
0.2%
Other values (527) 527
91.5%
ValueCountFrequency (%)
0 37
6.4%
65 1
 
0.2%
412 1
 
0.2%
483 1
 
0.2%
1215 1
 
0.2%
1471 1
 
0.2%
1814 1
 
0.2%
2620 1
 
0.2%
2733 1
 
0.2%
2996 1
 
0.2%
ValueCountFrequency (%)
9073952 1
0.2%
9066409 1
0.2%
8855330 1
0.2%
8712212 1
0.2%
8624570 1
0.2%
8488726 1
0.2%
8223984 1
0.2%
8192362 1
0.2%
7912220 1
0.2%
7826825 1
0.2%

beneficiary_count_lis
Categorical

HIGH CARDINALITY  MISSING 

Distinct275
Distinct (%)93.5%
Missing282
Missing (%)49.0%
Memory size4.6 KiB
0
 
19
184
 
2
287,370
 
1
833,933
 
1
8,128
 
1
Other values (270)
270 

Length

Max length7
Median length6
Mean length5.6802721
Min length1

Characters and Unicode

Total characters1670
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique273 ?
Unique (%)92.9%

Sample

1st row287,370
2nd row303,264
3rd row310,592
4th row317,698
5th row326,791

Common Values

ValueCountFrequency (%)
0 19
 
3.3%
184 2
 
0.3%
287,370 1
 
0.2%
833,933 1
 
0.2%
8,128 1
 
0.2%
7,877 1
 
0.2%
37 1
 
0.2%
704,238 1
 
0.2%
795,701 1
 
0.2%
866,303 1
 
0.2%
Other values (265) 265
46.0%
(Missing) 282
49.0%

Length

2023-09-18T19:22:42.065825image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 19
 
6.5%
184 2
 
0.7%
4,557 1
 
0.3%
745,678 1
 
0.3%
620 1
 
0.3%
606,135 1
 
0.3%
668,600 1
 
0.3%
734,070 1
 
0.3%
736,590 1
 
0.3%
737,603 1
 
0.3%
Other values (265) 265
90.1%

Most occurring characters

ValueCountFrequency (%)
, 261
15.6%
3 181
10.8%
1 180
10.8%
2 171
10.2%
0 132
7.9%
4 132
7.9%
7 129
7.7%
5 128
7.7%
6 128
7.7%
9 117
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1409
84.4%
Other Punctuation 261
 
15.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 181
12.8%
1 180
12.8%
2 171
12.1%
0 132
9.4%
4 132
9.4%
7 129
9.2%
5 128
9.1%
6 128
9.1%
9 117
8.3%
8 111
7.9%
Other Punctuation
ValueCountFrequency (%)
, 261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1670
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 261
15.6%
3 181
10.8%
1 180
10.8%
2 171
10.2%
0 132
7.9%
4 132
7.9%
7 129
7.7%
5 128
7.7%
6 128
7.7%
9 117
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1670
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 261
15.6%
3 181
10.8%
1 180
10.8%
2 171
10.2%
0 132
7.9%
4 132
7.9%
7 129
7.7%
5 128
7.7%
6 128
7.7%
9 117
7.0%

beneficiary_count_no_lis
Categorical

HIGH CARDINALITY  MISSING 

Distinct274
Distinct (%)93.2%
Missing282
Missing (%)49.0%
Memory size4.6 KiB
0
 
19
6,462
 
2
6,252
 
2
51,256
 
1
650,078
 
1
Other values (269)
269 

Length

Max length9
Median length7
Mean length5.5782313
Min length1

Characters and Unicode

Total characters1640
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique271 ?
Unique (%)92.2%

Sample

1st row51,256
2nd row57,406
3rd row66,009
4th row79,066
5th row78,369

Common Values

ValueCountFrequency (%)
0 19
 
3.3%
6,462 2
 
0.3%
6,252 2
 
0.3%
51,256 1
 
0.2%
650,078 1
 
0.2%
4,769 1
 
0.2%
28 1
 
0.2%
423,708 1
 
0.2%
610,098 1
 
0.2%
556,827 1
 
0.2%
Other values (264) 264
45.8%
(Missing) 282
49.0%

Length

2023-09-18T19:22:42.261245image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 19
 
6.5%
6,252 2
 
0.7%
6,462 2
 
0.7%
6,548 1
 
0.3%
5,000 1
 
0.3%
51,726 1
 
0.3%
5,072 1
 
0.3%
66,009 1
 
0.3%
79,066 1
 
0.3%
78,369 1
 
0.3%
Other values (264) 264
89.8%

Most occurring characters

ValueCountFrequency (%)
, 265
16.2%
4 158
9.6%
6 154
9.4%
1 148
9.0%
3 147
9.0%
0 143
8.7%
5 143
8.7%
2 142
8.7%
8 124
7.6%
9 120
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1375
83.8%
Other Punctuation 265
 
16.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 158
11.5%
6 154
11.2%
1 148
10.8%
3 147
10.7%
0 143
10.4%
5 143
10.4%
2 142
10.3%
8 124
9.0%
9 120
8.7%
7 96
7.0%
Other Punctuation
ValueCountFrequency (%)
, 265
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1640
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 265
16.2%
4 158
9.6%
6 154
9.4%
1 148
9.0%
3 147
9.0%
0 143
8.7%
5 143
8.7%
2 142
8.7%
8 124
7.6%
9 120
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1640
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 265
16.2%
4 158
9.6%
6 154
9.4%
1 148
9.0%
3 147
9.0%
0 143
8.7%
5 143
8.7%
2 142
8.7%
8 124
7.6%
9 120
7.3%

beneficiary_cost_share_lis
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct276
Distinct (%)93.9%
Missing282
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean2791453.5
Minimum0
Maximum24982823
Zeros19
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:42.482563image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1216766.93
median626085.41
Q31722618.9
95-th percentile14304288
Maximum24982823
Range24982823
Interquartile range (IQR)1505852

Descriptive statistics

Standard deviation4805260.3
Coefficient of variation (CV)1.7214187
Kurtosis4.4272993
Mean2791453.5
Median Absolute Deviation (MAD)525587.45
Skewness2.1996712
Sum8.2068734 × 108
Variance2.3090526 × 1013
MonotonicityNot monotonic
2023-09-18T19:22:42.728875image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
3.3%
6931345.55 1
 
0.2%
17249020.38 1
 
0.2%
77166.52 1
 
0.2%
160794.65 1
 
0.2%
47916.72 1
 
0.2%
311872.21 1
 
0.2%
33.8 1
 
0.2%
9947779.77 1
 
0.2%
19885808.24 1
 
0.2%
Other values (266) 266
46.2%
(Missing) 282
49.0%
ValueCountFrequency (%)
0 19
3.3%
33.8 1
 
0.2%
178.7 1
 
0.2%
2566.24 1
 
0.2%
3112.21 1
 
0.2%
4780.77 1
 
0.2%
9505.49 1
 
0.2%
10830.44 1
 
0.2%
15034.15 1
 
0.2%
17539.41 1
 
0.2%
ValueCountFrequency (%)
24982823.48 1
0.2%
23730069.38 1
0.2%
20787921.15 1
0.2%
20627810.78 1
0.2%
19885808.24 1
0.2%
17249020.38 1
0.2%
16207465.22 1
0.2%
15743573.5 1
0.2%
15631281.7 1
0.2%
15449270.92 1
0.2%

beneficiary_cost_share_no_lis
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct276
Distinct (%)93.9%
Missing282
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean41548691
Minimum0
Maximum3.7454264 × 108
Zeros19
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:42.976677image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12531342.9
median13354509
Q340321755
95-th percentile1.8214768 × 108
Maximum3.7454264 × 108
Range3.7454264 × 108
Interquartile range (IQR)37790412

Descriptive statistics

Standard deviation67263523
Coefficient of variation (CV)1.6189084
Kurtosis6.4345253
Mean41548691
Median Absolute Deviation (MAD)12029032
Skewness2.4204238
Sum1.2215315 × 1010
Variance4.5243816 × 1015
MonotonicityNot monotonic
2023-09-18T19:22:43.214367image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
3.3%
31255332.42 1
 
0.2%
152539250.2 1
 
0.2%
915495.6 1
 
0.2%
1761959.46 1
 
0.2%
468831.45 1
 
0.2%
14672832.69 1
 
0.2%
33830.27 1
 
0.2%
90017704.75 1
 
0.2%
180156162.5 1
 
0.2%
Other values (266) 266
46.2%
(Missing) 282
49.0%
ValueCountFrequency (%)
0 19
3.3%
27868.45 1
 
0.2%
33830.27 1
 
0.2%
230364.44 1
 
0.2%
275824.96 1
 
0.2%
281066.46 1
 
0.2%
333503.38 1
 
0.2%
374583.58 1
 
0.2%
390428.09 1
 
0.2%
400618.96 1
 
0.2%
ValueCountFrequency (%)
374542642.5 1
0.2%
370273860.1 1
0.2%
355047463.8 1
0.2%
331466334.3 1
0.2%
270949753.5 1
0.2%
237177177.4 1
0.2%
219440128.4 1
0.2%
206346787.7 1
0.2%
203452124.7 1
0.2%
200403245 1
0.2%

average_beneficiary_cost_share_lis
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct264
Distinct (%)89.8%
Missing282
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean37.371361
Minimum0
Maximum216
Zeros19
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:43.456741image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.345
median21.465
Q341.2825
95-th percentile140.237
Maximum216
Range216
Interquartile range (IQR)33.9375

Descriptive statistics

Standard deviation44.798235
Coefficient of variation (CV)1.1987317
Kurtosis2.0961139
Mean37.371361
Median Absolute Deviation (MAD)14.405
Skewness1.6953593
Sum10987.18
Variance2006.8818
MonotonicityNot monotonic
2023-09-18T19:22:43.686574image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
3.3%
21.11 2
 
0.3%
24.2 2
 
0.3%
116.31 2
 
0.3%
25.14 2
 
0.3%
7.08 2
 
0.3%
7.43 2
 
0.3%
21.68 2
 
0.3%
20.52 2
 
0.3%
8.89 2
 
0.3%
Other values (254) 257
44.6%
(Missing) 282
49.0%
ValueCountFrequency (%)
0 19
3.3%
0.91 1
 
0.2%
1.2 1
 
0.2%
1.85 1
 
0.2%
1.91 1
 
0.2%
2.06 1
 
0.2%
2.14 1
 
0.2%
2.16 1
 
0.2%
2.33 1
 
0.2%
2.38 1
 
0.2%
ValueCountFrequency (%)
216 1
0.2%
211.13 1
0.2%
178.81 1
0.2%
173.57 1
0.2%
166.15 1
0.2%
163.52 1
0.2%
157.02 1
0.2%
155.83 1
0.2%
154.28 1
0.2%
149.76 1
0.2%

average_beneficiary_cost_share_no_lis
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct276
Distinct (%)93.9%
Missing282
Missing (%)49.0%
Infinite0
Infinite (%)0.0%
Mean1166.0669
Minimum0
Maximum8007.22
Zeros19
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:43.917073image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q158.005
median340.465
Q31689.7775
95-th percentile4227.1795
Maximum8007.22
Range8007.22
Interquartile range (IQR)1631.7725

Descriptive statistics

Standard deviation1559.8512
Coefficient of variation (CV)1.337703
Kurtosis1.7893213
Mean1166.0669
Median Absolute Deviation (MAD)317.185
Skewness1.5511698
Sum342823.67
Variance2433135.8
MonotonicityNot monotonic
2023-09-18T19:22:44.154225image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 19
 
3.3%
609.79 1
 
0.2%
250.02 1
 
0.2%
87.39 1
 
0.2%
74.2 1
 
0.2%
75.69 1
 
0.2%
3076.71 1
 
0.2%
1208.22 1
 
0.2%
212.45 1
 
0.2%
277.13 1
 
0.2%
Other values (266) 266
46.2%
(Missing) 282
49.0%
ValueCountFrequency (%)
0 19
3.3%
10.96 1
 
0.2%
11.18 1
 
0.2%
11.84 1
 
0.2%
11.88 1
 
0.2%
12.1 1
 
0.2%
12.38 1
 
0.2%
13.46 1
 
0.2%
14.53 1
 
0.2%
15.54 1
 
0.2%
ValueCountFrequency (%)
8007.22 1
0.2%
6836.69 1
0.2%
6129.72 1
0.2%
5655.3 1
0.2%
5553.34 1
0.2%
5497.09 1
0.2%
5204.8 1
0.2%
5120.22 1
0.2%
5053.61 1
0.2%
5028.09 1
0.2%

serialid
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct576
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288.5
Minimum1
Maximum576
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 KiB
2023-09-18T19:22:44.422429image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile29.75
Q1144.75
median288.5
Q3432.25
95-th percentile547.25
Maximum576
Range575
Interquartile range (IQR)287.5

Descriptive statistics

Standard deviation166.42115
Coefficient of variation (CV)0.57684975
Kurtosis-1.2
Mean288.5
Median Absolute Deviation (MAD)144
Skewness0
Sum166176
Variance27696
MonotonicityStrictly increasing
2023-09-18T19:22:44.655060image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
2 1
 
0.2%
381 1
 
0.2%
382 1
 
0.2%
383 1
 
0.2%
384 1
 
0.2%
385 1
 
0.2%
386 1
 
0.2%
387 1
 
0.2%
388 1
 
0.2%
Other values (566) 566
98.3%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
576 1
0.2%
575 1
0.2%
574 1
0.2%
573 1
0.2%
572 1
0.2%
571 1
0.2%
570 1
0.2%
569 1
0.2%
568 1
0.2%
567 1
0.2%

Interactions

2023-09-18T19:22:33.061859image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:01.986211image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:04.500184image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:07.198324image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:10.756322image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:13.723034image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:16.057194image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:18.509323image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:20.640985image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:22.734891image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:24.859304image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:26.805707image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:28.992582image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:30.863126image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:33.210396image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:02.144884image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:04.686472image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:07.432017image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:10.962070image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:13.871480image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:16.238272image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:18.652030image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:20.781354image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:22.869508image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:25.000720image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:26.944445image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:29.123249image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:31.161095image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:33.355749image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:02.332704image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:04.848818image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:07.726978image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:11.216362image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:14.030786image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:16.425546image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:18.819649image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:20.926955image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:23.024677image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:25.137848image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:27.095717image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:29.253817image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:31.319336image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:33.504834image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:02.583192image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:05.009069image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:07.962763image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:11.440126image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:14.188483image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:16.578172image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:18.960422image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:21.062649image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:23.156028image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:25.282534image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:27.245656image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:29.399281image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:31.465204image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:33.636767image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:02.812925image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:05.170703image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:08.193066image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:11.654132image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:14.340941image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:16.761375image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:19.106636image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:21.196518image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:23.290462image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:25.408267image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:27.385629image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:29.522640image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:31.607587image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:33.794455image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:02.977052image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:05.363502image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:08.445847image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:11.903603image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:14.513848image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:16.933537image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:19.266319image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:21.338078image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:23.439436image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:25.572816image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:27.532074image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:29.660358image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:31.757279image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:33.948622image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:03.211745image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:05.547586image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:08.702301image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:12.151859image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:14.683471image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:17.100787image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:19.439065image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:21.673779image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:23.593375image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:25.720865image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:27.689449image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:29.806204image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:31.921617image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:34.098716image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:03.385413image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:05.897386image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:08.942920image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:12.402512image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:15.020322image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:17.265766image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:19.600496image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:21.830729image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:23.737500image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:25.860197image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:28.000443image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:29.943842image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:32.071076image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:34.394140image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:03.558495image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:06.136272image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:09.166034image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:12.611279image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:15.158586image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:17.420346image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:19.748591image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:21.980799image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:23.883830image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:25.985843image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:28.124634image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:30.060930image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:32.195387image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:34.526673image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:03.709297image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:06.297577image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:09.380826image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:12.805077image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:15.312451image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:17.567656image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:19.900050image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:22.120436image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:24.012528image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:26.115676image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:28.256079image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:30.193555image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:32.334918image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:34.653033image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:03.845249image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:06.450804image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:09.605288image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:13.005085image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:15.465051image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:17.721239image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:20.035979image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:22.234155image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:24.139690image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:26.244857image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:28.404403image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:30.316494image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:32.481230image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:34.798219image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:04.006875image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:06.606688image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:09.842715image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:13.215983image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:15.621424image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:17.887805image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:20.189630image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:22.351366image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:24.282748image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:26.391018image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:28.550732image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:30.451067image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:32.632934image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:34.924586image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:04.151422image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:06.745111image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:10.053837image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:13.413701image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:15.756306image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:18.023788image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:20.321660image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:22.471187image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:24.417655image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:26.520605image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:28.695792image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:30.590532image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:32.765801image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:35.068800image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:04.316818image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:06.942908image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:10.305891image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:13.578083image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:15.912070image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:18.345981image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:20.494682image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:22.596437image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:24.554735image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:26.661420image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:28.848463image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:30.735554image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
2023-09-18T19:22:32.923745image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/

Correlations

2023-09-18T19:22:45.056781image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
yeartotal_spendingbeneficiary_countunit_countbeneficiary_cost_sharetotal_annual_spending_per_useraverage_cost_per_unitaverage_annual_beneficiary_cost_shareclaim_countbeneficiary_cost_share_lisbeneficiary_cost_share_no_lisaverage_beneficiary_cost_share_lisaverage_beneficiary_cost_share_no_lisserialidcoverage_type
year1.0000.2780.1300.1250.2010.2350.1980.1930.1270.1160.2290.1380.2080.0030.000
total_spending0.2781.0000.3760.3900.8600.5380.4880.5680.4740.7810.8930.6200.5230.0180.212
beneficiary_count0.1300.3761.0000.6670.359-0.409-0.210-0.5260.9530.8520.624-0.187-0.350-0.3350.241
unit_count0.1250.3900.6671.0000.463-0.135-0.4320.1740.7600.7520.544-0.183-0.351-0.2480.347
beneficiary_cost_share0.2010.8600.3590.4631.0000.4690.3400.5520.4530.8590.9990.5180.3310.0010.285
total_annual_spending_per_user0.2350.538-0.409-0.1350.4691.0000.7640.999-0.3020.0100.3110.8900.9820.2430.286
average_cost_per_unit0.1980.488-0.210-0.4320.3400.7641.0000.466-0.2260.0560.3140.8210.9300.2170.121
average_annual_beneficiary_cost_share0.1930.568-0.5260.1740.5520.9990.4661.000-0.334NaNNaNNaNNaN0.4021.000
claim_count0.1270.4740.9530.7600.453-0.302-0.226-0.3341.0000.8930.655-0.121-0.288-0.3330.341
beneficiary_cost_share_lis0.1160.7810.8520.7520.8590.0100.056NaN0.8931.0000.8440.2080.044-0.2271.000
beneficiary_cost_share_no_lis0.2290.8930.6240.5440.9990.3110.314NaN0.6550.8441.0000.5280.342-0.0651.000
average_beneficiary_cost_share_lis0.1380.620-0.187-0.1830.5180.8900.821NaN-0.1210.2080.5281.0000.9210.1521.000
average_beneficiary_cost_share_no_lis0.2080.523-0.350-0.3510.3310.9820.930NaN-0.2880.0440.3420.9211.0000.1651.000
serialid0.0030.018-0.335-0.2480.0010.2430.2170.402-0.333-0.227-0.0650.1520.1651.0000.319
coverage_type0.0000.2120.2410.3470.2850.2860.1211.0000.3411.0001.0001.0001.0000.3191.000

Missing values

2023-09-18T19:22:35.320252image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-18T19:22:35.954075image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-18T19:22:36.501173image/svg+xmlMatplotlib v3.4.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

yearbrand_namegeneric_namecoverage_typetotal_spendingbeneficiary_countunit_countbeneficiary_cost_sharetotal_annual_spending_per_useraverage_cost_per_unitaverage_annual_beneficiary_cost_shareclaim_countbeneficiary_count_lisbeneficiary_count_no_lisbeneficiary_cost_share_lisbeneficiary_cost_share_no_lisaverage_beneficiary_cost_share_lisaverage_beneficiary_cost_share_no_lisserialid
02010AbilifyAripiprazolePart D1.225885e+093386267184819038186677.973620.1717.06NaN2296081287,37051,2566931345.5531255332.4224.12609.791
12011AbilifyAripiprazolePart D1.469590e+093606707747175633752894.084074.6118.97NaN2447867303,26457,4067000137.5426752756.5423.08466.032
22012AbilifyAripiprazolePart D1.758054e+093766018216170039700089.434668.2121.40NaN2572017310,59266,0096594393.2633105696.1721.23501.533
32013AbilifyAripiprazolePart D2.107092e+093967648615030046564692.855310.6924.46NaN2886837317,69879,0666872985.0939691707.7621.63502.014
42014AbilifyAripiprazolePart D2.527319e+094051618819869149598419.686237.8328.65NaN2964075326,79178,3696334112.6343264307.0519.38552.065
52015AbilifyAripiprazolePart D1.572428e+093225824785504732007792.274874.5133.12NaN1635610269,55853,0244447124.4927560667.7816.50519.786
62010AbraxanePaclitaxel Protein-BoundPart B1.155539e+0868111237953122795393.6616965.789.333346.8554260NaNNaNNaNNaNNaNNaN7
72011AbraxanePaclitaxel Protein-BoundPart B1.213891e+0876601291912324065171.4915847.149.403141.6755813NaNNaNNaNNaNNaNNaN8
82012AbraxanePaclitaxel Protein-BoundPart B1.341285e+0883921408512526648986.3315982.909.523175.5260473NaNNaNNaNNaNNaNNaN9
92013AbraxanePaclitaxel Protein-BoundPart B2.130076e+08140912266031042963406.5215116.579.403049.0097001NaNNaNNaNNaNNaNNaN10
yearbrand_namegeneric_namecoverage_typetotal_spendingbeneficiary_countunit_countbeneficiary_cost_sharetotal_annual_spending_per_useraverage_cost_per_unitaverage_annual_beneficiary_cost_shareclaim_countbeneficiary_count_lisbeneficiary_count_no_lisbeneficiary_cost_share_lisbeneficiary_cost_share_no_lisaverage_beneficiary_cost_share_lisaverage_beneficiary_cost_share_no_lisserialid
5662012YervoyIpilimumabPart B1.939894e+082101155147620707885.0192331.93125.049856.206126NaNNaNNaNNaNNaNNaN567
5672013YervoyIpilimumabPart B2.166421e+082349173097722259232.4692227.37125.169476.056701NaNNaNNaNNaNNaNNaN568
5682014YervoyIpilimumabPart B2.653790e+082881205997627560982.1892113.50128.839566.468026NaNNaNNaNNaNNaNNaN569
5692015YervoyIpilimumabPart B2.171237e+082340162871322027616.5192787.90133.319413.516326NaNNaNNaNNaNNaNNaN570
5702010ZytigaAbiraterone AcetatePart D0.000000e+00000.000.000.00NaN0000.000.000.000.00571
5712011ZytigaAbiraterone AcetatePart D7.100941e+07393616483046346405.3318041.0143.08NaN134779872,94950177.156296228.1850.842135.04572
5722012ZytigaAbiraterone AcetatePart D2.242602e+088357459407816650258.0526835.0148.82NaN379342,1056,252216616.8216433641.23102.912628.54573
5732013ZytigaAbiraterone AcetatePart D4.696617e+0814191863086927858755.3933095.7454.42NaN714233,32710,864312215.6727546539.7293.842535.58574
5742014ZytigaAbiraterone AcetatePart D7.070979e+08170441183615941003957.9341486.6159.74NaN984693,58513,459472187.3840531770.55131.713011.50575
5752015ZytigaAbiraterone AcetatePart D7.900497e+08169651202208944876300.0146569.3965.72NaN1003763,54113,424579011.3144297288.70163.523299.86576